Langfuse 2.0 has been launched as an advanced LLM Engineering Platform, expanding beyond its initial focus on production traces of LLM applications to now include features such as evaluations, prompt management, an LLM playground, and datasets. Originally introduced to address challenges in code-generation and scraping agents, Langfuse has grown to support thousands of teams by offering open-source tooling for complex LLM workflows. The platform provides scalable integrations with popular models and frameworks, leveraging its Python and Typescript SDKs. A key new feature is the eval service, which allows users to run model-based evaluations on their traces, facilitating the creation of large labeled datasets with minimal manual effort. The platform also enhances prompt workflow management, allowing for version control and monitoring through its SDKs and UI, and includes a new LLM playground for prompt iteration. The revamped Datasets feature supports structured evaluation processes, enabling users to run experiments and attach evaluations to datasets. Langfuse emphasizes flexibility and user-driven development, encouraging feedback and community contributions via GitHub Discussions.